Orthogonal frequency division multiplexing (OFDM) is a robust technique for efficiently transmitting data over a channel. The technique uses a plurality of sub-carrier frequencies (sub-carriers) within a channel bandwidth to transmit the data. In particular, these sub-carriers are arranged for optimal bandwidth efficiency compared to more conventional transmission approaches, such as frequency division multiplexing (FDM). Namely, the more conventional approaches waste large portions of the channel bandwidth to separate and isolate the sub-carrier frequency spectra, thereby avoiding inter-carrier interference (ICI). Notably, the frequency spectra of OFDM sub-carriers overlap significantly within the OFDM channel bandwidth. Despite this spectral overlap, OFDM allows, within a receiver, resolution and recovery of information originally modulated onto each sub-carrier.
These bandwidth efficiencies provided by OFDM, however, do result in some difficulties and practical limitations. In particular, OFDM systems are highly susceptible to frequency offset, phase noise perturbation, and clock offset. These impairments generally result in an unwanted rotation of the received sub-carriers of the OFDM signal (e.g., a rotation of the received constellations of the individual sub-carriers). The unwanted rotations can be attributed to a common phase rotation and a sub-carrier dependent phase rotation, which can result in inter-carrier interference (ICI). As long as the variance of the phase noise is small and the residual frequency offset is within 10 percent of the sub-carrier spacing, the effects of ICI can be ignored. In such a case, the effects of phase noise and residual frequency offset can often be lumped into a common rotation (CR) term, which affects all sub-carriers within an OFDM symbol equally. Phase noise generally refers to short-term random fluctuations in phase of an oscillator due to time-domain instabilities. Thus, the CR due to phase noise affects each OFDM symbol randomly, while the CR due to frequency offset accumulates over time.
The effect of the sampling clock offset, even though subtler, since it accumulates slowly over time, can be detrimental over a large number of symbols. The sampling clock generally refers to a timing source for an analog-to-digital (A/D) converter provided within an OFDM receiver. The A/D converter converts a baseband analog signal to samples representing a complex digital signal for further processing by the receiver. Since a time offset essentially translates to a sub-carrier dependent rotation (SDR) in the frequency domain, the sub-carriers at the edge of the OFDM spectrum can have their constellation rotated out of the reliable detection region. This can cause substantial packet error rates for higher-order digital modulations including quadrature amplitude modulation (QAM), such as 16 QAM or 64 QAM. Such higher-order modulation techniques are commonly used in OFDM systems. For example, OFDM systems configured to communicate using protocols described in the Institute of Electrical and Electronics Engineers (IEEE) 802.11a wireless local-area network (WLAN) standard can use BPSK, QPSK, 16 QAM, and 64 QAM modulations, with data throughput rates ranging from 6 to 54 megabits per second (Mbps).
One particular problem, however, associated with prior art solutions is their related cost and technical complexity. Prior art solutions to timing, frequency, and phase errors include the provision of highly-stable timing reference (e.g., a rubidium clock) that can maintain timing at a receiver accurately with respect to a remote transmitter. Other solutions include using a separate channel to broadcast precise timing information to the receivers. This approach unnecessarily wastes channel bandwidth. Yet other prior art solutions include providing precision phase locked loops (PLL) within the receiver.
Unfortunately, the problems related to cost and complexity of the prior art solutions are amplified in WLAN applications. WLAN systems generally rely on a limited number of access points, each capable of communicating with a large number of remote users. It is the cost and complexity of the remote users that must be kept to a minimum to ensure public acceptance and profitability.